Cloud Computing
M. Farmani; S. Farnam; M. J. Khani; Z. Torabi; Z. Shirmohammadi
Abstract
Background and Objectives: With the increase of population in the world along with the decrease of natural resources, agricultural land and the increase of unpredictable environmental conditions, it causes concerns in the field of food supply, which is one of the serious concerns for all countries of ...
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Background and Objectives: With the increase of population in the world along with the decrease of natural resources, agricultural land and the increase of unpredictable environmental conditions, it causes concerns in the field of food supply, which is one of the serious concerns for all countries of the world. Therefore, the agricultural industry has moved towards smart agriculture. Smart agriculture using the Internet of Things, which uses different types of sensors to collect data (such as temperature, humidity, light, etc.), a communication network to send and receive data, and information systems to manage and analyze data. Smart agriculture deals with a huge amount of data collected from farms, which has fundamental challenges for analysis using old systems such as lack of storage space, processing delay. Computational paradigm is a key solution to solve the problems of time delay, security, storage space management, real-time analysis. Computing paradigms include cloud, fog and edge computing, which by combining each of them in smart agriculture has caused a great transformation in this industry. The purpose of this article is to provide a comprehensive review of the architecture of computing paradigms in smart agriculture applications.Methods: To achieve the goals of this article, the methodology is divided into two parts: article selection and review of the selected articles. The computational paradigms used in the selected articles are from 2019 to 2022. Each selected paper is then reviewed in detail in terms of categories of computing paradigms, architectures, key points, advantages, and challenges.Results: Computational paradigms have significant advantages. Combining these paradigms with each other in a complementary way covers many challenges. The architecture based on the combination of edge-fog-cloud computing is one of the best architectures combined with smart agriculture.Conclusion: By combining computing paradigms and smart agriculture, the challenges based on traditional and old systems are overcome. Combining these paradigms complement each other's challenges.
Computer Architecture
Z. Torabi; Armin Belghadr
Abstract
Background and Objectives: Residue number system (RNS) is considered as a prominent candidate for high-speed arithmetic applications due to its limited carry propagation, fault tolerance, and parallelism in “Addition”, “Subtraction”, and “Multiplication” operations. ...
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Background and Objectives: Residue number system (RNS) is considered as a prominent candidate for high-speed arithmetic applications due to its limited carry propagation, fault tolerance, and parallelism in “Addition”, “Subtraction”, and “Multiplication” operations. Whereas, “Comparison”, “Division”, “Scaling”, “Overflow Detection” and “Sign Detection” are considered as complicated operations in residue number systems, which have also received a surge of attention in a multitude of publications. Efficient realization of Comparators facilitates other hard-to-implement operations and extends the spectrum of RNS applications. Such comparators can substitute the straightforward method (i.e. converting the comparison operands to binary and comparing them with wide word binary comparators) to compare RNS numbers. Methods: Dynamic Range Partitioning (DRP) method has shown advantages for comparing unsigned RNS numbers in the 3-moduli sets {2^n,2^n±1} and {2^n,2^n-〖1,2〗^(n+1)-1}, in comparison with other methods. In this paper, we employed DRP components and designed a unified unit that detects the sign of operands and also compares numbers, for the 5-moduli set γ={2^2n,2^n±1,2^n±3}. This unit can be used for comparison of signed and also unsigned RNS numbers in the moduli set γ.Results: Synthesized comparison results reveal 47% (54%) speed-up, 35% (32%) less area consumption, 25% (24%) lower power dissipation, and 60% (65%) less energy for n=8 (16) in comparison to the straightforward signed comparator. Conclusion: According to the results of this study, DRP method for sign detection and comparison operations outperforms other methods in different moduli sets including 5-moduli set γ={2^2n,2^n±1,2^n±3}.
